Distribute Application Workloads Across Multiple Clouds & Data Centers


Even with the rapid deployment of applications to the cloud, there is a lack of reliability in distributing user requests to application services across multiple clouds and hybrid deployments. Uneven distribution results in unoptimized compute costs, degraded application performance and poor user experience.

Whenever applications are deployed to multiple locations (physical or virtualized data centers and/or public cloud), some algorithm is required to distribute the user requests for content across all available application service instances to provide optimal experience. The applications may be in the same geographical location or in different geography.

Global Server Load Balancing

Global Server Load Balancing (GSLB), traditionally a DNS-based user redirection, allows service providers to optimize resource use, maximize throughput, minimize response time, and avoid overload of any computing resource.

GSLB is the act of redirecting users requesting content to specific application instances that are closest to users based on some distribution logic. A GSLB device performs application service selection (based on prescribed or inferred criteria) to direct user traffic to the best available service instance for a given domain.

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While there are several use cases for GSLB in application environments, GSLB is most commonly are implemented to achieve one or more of the following goals for an application:

  • Reduced latency to users in geographically distributed locations by optimizing request distribution
  • Offer tolerance across application, network or cloud/data center failures
  • Enable non-disruptive migration to another cloud and other data centers

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GSLB allows service providers to enhance user experience by reducing response times. At the same time, providers deploying GSLB benefit by increasing service availability while reducing expensive data connections by directing users to the best site by intelligently monitoring site’s health, proximity and response time.

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